Inverse planning using optimization constraints derived from image intensity

ABSTRACT

A method of automatically identifying a region of differing intensity in a functional image is described.

REFERENCE TO RELATED APPLICATION

This application is a continuation of Ser. No. 11/144,235 filed Jun. 2,2005, which is hereby incorporated by reference.

TECHNICAL FIELD

This invention relates to the field of radiation treatment, and inparticular, to inverse planning in radiation treatment.

BACKGROUND

Tumors and lesions are types of pathological anatomies characterized byabnormal growth of tissue resulting from the uncontrolled, progressivemultiplication of cells, while serving no physiological function.

A non-invasive method for pathological anatomy treatment is externalbeam radiation therapy. In one type of external beam radiation therapy,an external radiation source is used to direct a sequence of x-ray beamsat a tumor site from multiple angles, with the patient positioned so thetumor is at the center of rotation (isocenter) of the beam. As the angleof the radiation source is changed, every beam passes through the tumorsite, but passes through a different area of healthy tissue on its wayto the tumor. As a result, the cumulative radiation dose at the tumor ishigh and the average radiation dose to healthy tissue is low. The termradiotherapy refers to a procedure in which radiation is applied to atarget region for therapeutic, rather than necrotic, purposes. Theamount of radiation utilized in radiotherapy treatment sessions istypically about an order of magnitude smaller, as compared to the amountused in a radiosurgery session. Radiotherapy is typically characterizedby a low dose per treatment (e.g., 100-200 centi-Grays (cGy)), shorttreatment times (e.g., 10 to 30 minutes per treatment) andhyperfractionation (e.g., 30 to 45 days of treatment). For convenience,the term “radiation treatment” is used herein to mean radiosurgeryand/or radiotherapy unless otherwise noted by the magnitude of theradiation.

Conventional isocentered radiosurgery systems (e.g., the Gamma Knife)use forward treatment planning. That is, a medical physicist determinesthe radiation dose to be applied to a tumor and then calculates how muchradiation will be absorbed by critical structures and other healthytissue. There is no independent control of the two dose levels, for agiven number of beams, because the volumetric energy density at anygiven distance from the isocenter is a constant, no matter where theisocenter is located.

Inverse planning, in contrast to forward planning, allows the medicalphysicist to independently specify the minimum tumor dose and themaximum dose to other healthy tissues, and lets the treatment planningsoftware select the direction, distance, and total number and energy ofthe beams. Conventional treatment planning software packages aredesigned to import 3-D images from a diagnostic imaging source, forexample, computerized x-ray tomography (CT) scans. CT is able to providean accurate three-dimensional model of a volume of interest (e.g., skullor other tumor bearing portion of the body) generated from a collectionof CT slices and, thereby, the volume requiring treatment can bevisualized in three dimensions.

During inverse planning, a volume of interest (VOI) is used to delineatestructures to be targeted or avoided with respect to the administeredradiation dose. That is, the radiation source is positioned in asequence calculated to localize the radiation dose into a VOI that asclosely as possible conforms to the tumor requiring treatment, whileavoiding exposure of nearby healthy tissue. Once the target (e.g.,tumor) VOI has been defined, and the critical and soft tissue volumeshave been specified, the responsible radiation oncologist or medicalphysicist specifies the minimum radiation dose to the target VOI and themaximum dose to normal and critical healthy tissue. The software thenproduces the inverse treatment plan, relying on the positionalcapabilities of the radiation treatment system, to meet the min/max doseconstraints of the treatment plan.

The two principal requirements for an effective radiation treatmentsystem are conformality and homogeneity. Homogeneity is the uniformityof the radiation dose over the volume of the target (e.g., pathologicalanatomy such as a tumor, lesion, vascular malformation, etc.)characterized by a dose volume histogram (DVH). An ideal DVH would be arectangular function, where the dose is 100 percent of the prescribeddose over the volume of the tumor and zero elsewhere.

Conformality is the degree to which the radiation dose matches(conforms) to the shape and extent of the target (e.g., tumor) in orderto avoid damage to critical adjacent structures. More specifically,conformality is a measure of the amount of prescription (Rx) dose(amount of dose applied) within a target VOI. Conformality may bemeasured using a conformality index (CI)=total volume at >=Rxdose/target volume at >=Rx dose. Perfect conformality results in a CI=1.With conventional radiotherapy treatment, using treatment planningsoftware, a clinician identifies a dose isocontour for a correspondingVOI for application of a treatment dose (e.g., 2000 cGy).

FIG. 1 illustrates the graphical output of treatment planning softwaredisplaying a slice of a CT image a containing pathological anatomy(e.g., tumor, lesion, etc.) region and normal anatomy as a criticalregion (e.g., internal organ) to be avoided by radiation. The treatmentplanning software enables the generation of a critical region contour, atarget (i.e., pathological anatomy) region contour, and a doseisocontour on the displayed CT slice. Conventionally, a user manuallydelineates points (e.g., some of the dots on the contour lines ofFIG. 1) on the display that is used by the treatment planning softwareto generate the corresponding contours. While this may seem an easytask, such matching is difficult due to the 3 dimensional nature andirregularities of the pathological and normal anatomies.

Another problem with conventional planning methods is that it may bedifficult to achieve the best possible conformality when relying solelyon anatomical images on which to base dose constraints because theseimages provide no information related to current understandings oflesions at the molecular and chemical level. Advances in imaging nowoffer other types of image modalities to include “functional”information about a lesion, such as biological and mechanistic data. Forexample, positron emission tomography (PET) images can provide metabolicinformation about a pathological anatomy such as a lesion. Functionalmagnetic resonance imaging (FMRI) visualizes changes in the chemicalcomposition of brain areas or changes in the flow of fluids. In PETimages, the brightness of different areas of the image may be related tocell density. That is, the greater the brightness in a particularregion, the higher the density of lesion cells in that region. It maythen be desirable to deliver higher doses of radiation to certainregions of the lesion based on the functional image data. However, someconventional external beam radiation systems may not be able to deliverradiation dose accurately enough to discriminate among such regionswithin a lesion or tumor, thereby making such identificationsunnecessary.

Moreover, despite advances in functional imaging and radiation dosedelivery, an operator or physician must go through a number of tediousand time consuming steps to optimize a treatment plan based on combiningfunctional image data with anatomical image data. For example, thephysician would have to visually compare a CT image and a PET image ofthe same VOI, and determine which region of the CT image corresponds toa region of high lesion cell density shown on the PET image based on avisual inspection of different areas of brightness on the PET image.After this determination is made, the physician then would have tomanually delineate the visually identified area of greater brightness(that may correspond to a region of high cell density). This process mayhave to be performed for multiple slices of the CT scan, making theplanning process very laborious and time consuming. Moreover, such amanual process that involves the visual inspection of a PET image by aperson may cause inaccuracies due to its subjective nature and thefallibility of the observing person.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates a conventional CT image with a delineated criticalregion and a delineated pathological anatomy region.

FIG. 2 is a flowchart showing one method of an inverse planning process.

FIG. 3 is a CT image illustrating an axial view of an intra cranialregion with a pathological anatomy that has been outlined for referencepurposes.

FIG. 4 illustrates a PET image of the axial view of the intra cranialregion from FIG. 3.

FIG. 5 illustrates one embodiment of pixel intensity data matrix for afunctional image.

FIG. 6 illustrates the PET image of FIG. 4 with one embodiment of anautomatically generated contour of a higher intensity region within thepathological anatomy delineated in the PET image.

FIG. 7A is a graph showing an acceptable dose minimum as a function ofintensity.

FIG. 7B is a graph showing the DVH for various treatment conditions.

FIG. 8 is a flowchart illustrating one embodiment of a method of inversetreatment planning.

FIG. 9 illustrates a medical diagnostic imaging system implementing oneembodiment of the present invention.

FIG. 10 illustrates the flagging of high intensity pixels for adelineated area.

FIG. 11 is another illustration of flagging high intensity pixels for adelineated area.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific systems, components, methods, etc. in orderto provide a thorough understanding of the present invention. It will beapparent, however, to one skilled in the art that these specific detailsneed not be employed to practice the present invention. In otherinstances, well-known components or methods have not been described indetail in order to avoid unnecessarily obscuring the present invention.

Embodiments of the present invention include various steps, which willbe described below. The steps of the present invention may be performedby hardware components or may be embodied in machine-executableinstructions, which may be used to cause a general-purpose orspecial-purpose processor programmed with the instructions to performthe steps. Alternatively, the steps may be performed by a combination ofhardware and software.

Embodiments of the present invention may be provided as a computerprogram product, or software, that may include a machine-readable mediumhaving stored thereon instructions, which may be used to program acomputer system (or other electronic devices) to perform a process. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Themachine-readable medium may include, but is not limited to, magneticstorage medium (e.g., floppy diskette); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read-only memory (ROM);random-access memory (RAM); erasable programmable memory (e.g., EPROMand EEPROM); flash memory; electrical, optical, acoustical, or otherform of propagated signal (e.g., carrier waves, infrared signals,digital signals, etc.); or other type of medium suitable for storingelectronic instructions.

Embodiments of the present invention may also be practiced indistributed computing environments where the machine-readable medium isstored on and/or executed by more than one computer system. In addition,the information transferred between computer systems may either bepulled or pushed across the communication medium connecting the computersystems, such as in a remote diagnosis or monitoring system. In remotediagnosis or monitoring, a user may utilize embodiments of the presentinvention to diagnose or monitor a patient despite the existence of aphysical separation between the user and the patient. In addition, thetreatment delivery system may be remote from the treatment planningsystem.

A treatment planning process is described that automatically identifiesa region of high cell density based on a corresponding region of highintensity from a functional image such as a PET or single photonemission computed tomography (SPECT) image. The region of high intensitycan be constrained to receive a higher dose of radiation relative toother regions of the pathological anatomy having low intensity. Forexample, it may be advantageous for a radiation treatment plan to directmore radiation in those areas of the lesion with higher cell density,which may exhibit as brighter regions in a PET image. Applying aconstant dose to the VOI does not provide enough radiation exposure toregions of the lesion containing higher concentration of lesion cells.Instead of an operator (e.g., a physician or oncologist) having todelineate these higher cell density regions for higher dosing during theinverse planning process manually, the areas of higher cell density areautomatically identified by the treatment planning software based on adifference in intensity levels in the pixels of the functional imagesuch as PET, SPECT, functional magnetic resonance imaging (fMRI), etc.

The intensity of the pixels in the functional image can be used asanother input to the inverse planning software to customize theradiotherapy process for a patient. In one embodiment, the functionalimage may be overlaid or registered on the anatomical image. Thefunctional image may be acquired in the same space as the anatomicalimage (e.g., using a PET/CT scanner). In an alternative embodiment, datafrom a functional image can be combined with other inputs for inverseplanning. For example, the contents of the functional image can beoverlaid or fused with an anatomical image (e.g., a CT or MRI).

As such, dose volume constraints for a delineated region from theanatomical image can be combined with dose constraints from thefunctional image to optimize a treatment plan during inverse planning.Alternatively, the anatomical image may not be required and only thefunctional image can be used in treatment planning.

In an alternative embodiment, inverse planning can also encompassforward planning. This “mixed” plan can include part of the treatmentdose generated using forward planning and part generated by inverseplanning. For ease of explanation, examples of inverse planning aredescribed herein in relation to radiotherapy of a lesion in the brain.The methods described herein may also be applied to the treatment oflesions or tumors in other organs or regions of the body where radiationtreatment is applicable.

FIG. 2 is a flowchart generally describing one method of inverseplanning using inputs from one or more imaging modalities. In oneembodiment, the method may begin with receiving an anatomical image ofthe VOI targeted for radiation treatment, step 201. The anatomical imageprovides structural representations of the VOI containing thepathological anatomy (e.g., lesion) targeted for treatment, as well assurrounding tissue. For example, in one embodiment, the anatomical imagecan be a CT image slice. A “slice” of the CT image (i.e., the region ofinterest) may be examined by the user to manually delineate the targetregion and the critical region (i.e., healthy tissue), followed byapplying a set of dose constraints for each region.

Another image of the VOI using a functional image modality may also bereceived, step 202. The functional image may of a modality such as a PETimage, SPECT image, fMRI image, etc. to provide functional data of thetreatment region, step 202. Functional or “biological” images broadlyinclude metabolic, biochemical, physiological categories, and alsoencompass molecular, genotypic, and phenotypic images. Functional imagesprovide data that cannot be derived from anatomical images. For example,functional imaging of the brain can be used to apply a constraint toavoid critical neurological structures or to target a specific area fortreatment.

A functional image may provide data about pathological anatomy such as alesion that may not be evident from an anatomical image because the cellcontent within the lesion is not uniform. Certain regions ofpathological anatomy such as a lesion can have higher concentrations ofcells relative to other regions. In PET images of lesions, regions ofhigh cell activity or metabolism, such as regions cancer cells, aredisplayed brighter relative to regions of low cell activity. In onemethod, as described in greater detail below, PET images displaydifferences in cell concentrations by the uptake of sugar molecules.

In one embodiment, the received functional image may be correlated withthe anatomical image, step 203 and then additional dose constraints canbe applied to the treatment region to further define the dosedistribution in inverse planning based on the functional imageinformation about the lesion. In one embodiment, for example, the imagesmay be correlated by overlaying the functional image with the anatomicalimage. Alternatively, the anatomical and functional images may becorrelated in other manners, such as by acquiring the images in the samespace (e.g., using a PET/CT scanner) or by registering the functionalimage with the anatomical images using techniques known in the art.

In step 204, the identification of the higher intensity region in thereceived functional image of step 202 is performed automatically (notmanually performed by the user visually identifying such region on thefunctional image). An algorithm may be used to automatically identifyone or more regions of differing (e.g., higher) intensity within thedelineated contour of the target region (pathological anatomy such as alesion) and generate a corresponding sub-contour (with correspondingdose constraints) for the differing (e.g., higher) intensity region(s)so that such sub-regiori(s) may receive different radiation doserelative to other region(s) (e.g., of lower intensity). It should benoted that the identification of the higher intensity region isautomatically performed in that it does not require (but does notpreclude) user intervention in the identification process. For example,the user may be prompted by the treatment planning software to selectwhether automatic identification is desired; the user may be providedthe option of manually assisting the identification; the user may beable to change one or more pixel data values (as discussed in moredetail below) during the identification process; etc.

In an alternative embodiment, the method of inverse planning may includeonly steps 202 and 204 such that the acquisition and receipt of ananatomical image, and its correlation with the functional image, are notperformed as indicated by the dashed arrow path in FIG. 2. In such anembodiment, a functional imaging using only a single imaging modalitymay be acquired and received, and used for both treatment planningpurposes and/or automatic identification of the higher intensity region.Although the functional imaging modality is been described above inrelation to specific examples of PET, SPECT and fMRI, it should be notedthat other imaging modalities may be used. In an alternative embodiment,for example, the functional image modality may be a CT imaging modalitywhere the patient has been injected with a contrast chemical (e.g., adye such as iodine bound with an enzyme) that bonds to the pathologicalanatomy in a manner that makes it visible on the CT (e.g., by generatinghigher intensity in the CT image for higher concentrations of cellsrelative to other regions).

FIG. 3 illustrates a CT image slice 400 of an axial view through anintra cranial region of a patient. CT image 400 is a computer generatedimage composed of pixels in which varying regions of intensity (e.g.,dark region 402) distinguish the various anatomical portions of thebrain. CT image 400 may also include a pathological anatomy (e.g.,lesion) 403 which as been outlined 404 in FIG. 3 for emphasis).

FIG. 4 illustrates a PET image 400 of the same intra cranial axial viewillustrated by CT image 400 of FIG. 3. PET images are consideredfunctional because they provide data relating to the chemicalfunctioning of tissue, as opposed to anatomical or structural dataprovided by CT images. As briefly described above, a lesion does notnecessarily have a uniform distribution of cells within the volumeoccupied by the lesion. Functional images such as PET scans can providedata relating to the differences in cell density within the lesionvolume. Fluorodeoxyglucose (FDG), a radioactive sugar molecule, is usedto produce images that demonstrate increased glucose metabolismassociated with regions of lesion activity. Because cancer cells growand divide more rapidly than normal cells, they metabolize more sugarfor fuel. This increased activity identifies them as cancer in FDG-PETscanning. For this procedure, the patient is injected with the FDG andlies in a PET camera for the imaging. Areas of activity from PET imagesare also represented by differences in image intensity, as representedby dark regions 402 and bright regions 501. Especially beneficial is thedata shown with respect to tissue activity for lesion 403, which shows ahigher intensity region 504, indicating an area of high glucosemetabolism, and therefore higher lesion cell content. In one embodiment,the area that corresponds to the higher intensity region 504 on the PETimage of FIG. 4 may not be readily visible on the CT image of FIG. 3(which has been manually outlined on FIG. 3 for reference purposes).

FIG. 5 illustrates one embodiment of pixel intensity data matrix for afunctional image. The data in a functional image 500 may be representedby a matrix, or grid, 308 of the intensity data values of the pixelsused to form the image. A portion of the image matrix 308, is only shownin FIG. 5 over the area containing the pathological anatomy for ease ofillustration and may actual extend over the entire image. The data valuefor each pixel in the matrix corresponds to a particular intensity ofthe image for that pixel. The intensity data values for one or more ofthe pixels may be used to determine differences in intensity among twoor more sub-regions (e.g., within the contour 404 in FIG. 6 for thepathological anatomy). The use of the pixel intensity data valuesprovides a more precise determination of differing cell density regionsthan the visual brightness of the image as visual “seen” by a personlooking at the functional image.

The following algorithm may be used to determine a difference inintensity based on the received intensity data values for each of thepixels in the matrix of a functional image:

Set count to zero: Set pixel intensity total to zero: For each slice:For each PET pixel within the delineated target area: Add pixelintensity to the total Increase count by 1 End End Set mean targetintensity to be pixel intensity total divided by count For each slice:For each PET pixel within the delineated target area: If pixelintensity>mean target intensity Flag pixel as being part of the highintensity area Else Flag pixel as not being part of the high intensityarea End End End

In the above algorithm a threshold value for pixel intensity iscalculated, followed by a comparison of each pixel of the delineatedarea against that threshold value. In particular, the threshold iscalculated by summing the intensity values for each pixel of thedelineated area, and dividing that total by the total number of pixels.As such, in this embodiment, the mean pixel value corresponds to thethreshold pixel value. If the pixel intensity is greater than thethreshold value, then that pixel is flagged as being part of the highintensity area. If the pixel intensity is less than the threshold value,then that pixel is flagged as not part of the high intensity area.

FIG. 10 is representative FIG. 1000 illustrating the flagging of eachpixel for a delineated area, as indicated by segmented line 1001.Delineated area includes 33 pixels (P1 through P33). According to thealgorithm, the threshold value, corresponding to the mean targetintensity, is calculated by summing the total intensity for P1-P33 anddividing by 33. Four pixels—P3, P4, P8, P9—are flagged as being part ofthe high intensity area because the pixel intensity value for each ofthese pixels is greater than the calculated mean target intensity.

In an alternative embodiment, the difference in intensity data valuesamong two or more sub-regions can be determined in other manners, forexample, independent of the total intensity value, the number of pixels,and/or a threshold value. For example, all the pixels within delineatedarea 1001 can be ranked according to increasing intensity value. If P22had the lowest intensity value and P3 had the highest intensity value,the pixels would be ranked from P22 at one end to P3 at the oppositeend. A predetermined number of the highest ranked pixels could then becategorized as being part of the high intensity area. For example, thetop ten ranked pixels would be flagged. Alternatively, a predeterminedpercentage of the top ranked pixels could be flagged as being part ofthe high intensity area. For example, it could be predetermined that thetop 20% of the pixels are flagged. According to the example provided byFIG. 10, pixels P3, P4, P8, and P9 are flagged as being part of a highintensity area because their pixel intensity values are the highestranked pixels or part of a designated top percentage of pixels. Inalternative embodiments, other algorithms including variations of thedetermining the threshold value may be used in flagging pixels as highintensity.

The different intensity regions identified by the algorithm may be usedto generate corresponding contours for each of the different intensitysub-regions automatically, for example, contour 609 of FIG. 6 for higherintensity sub-region 504. Referring again to FIG. 10, a contour 1002 isformed around high intensity pixels P3, P4, P8, and P9. In oneembodiment, a contour around a high intensity region is formedautomatically if the number of high intensity pixels within the contouris greater than the number of high intensity pixels outside the contour.As illustrated in FIG. 10, there are no high intensity pixels outside ofcontour 1002, so the group of high intensity pixels has been properlyidentified. In an alternative embodiment, the automatic contouring ofhigh intensity pixels can be based on pixel proximity. For example, asillustrated in a representative functional scan 1010 of FIG. 11, ninehigh intensity pixels have been flagged for delineated region 1003. Acontour 1004 is generated around pixels P3, P4, P8, and P9 despite thefact that there are additional high intensity pixels outside of contour1004 (i.e., P17, P21, P23, P29, and P31). As such, a requirement for thegeneration of contour 1004 can be that one high intensity pixel isadjacent to another high intensity pixel. In other embodiments,variation of pixel proximity or other criteria can be applied togenerate a contour automatically. It should be noted that one sub-regionhaving pixels with certain intensity data values could reside withinanother sub-region.

It should be noted that treatment planning software need not “see”(e.g., by optical image recognition) the function image but, rather,just receive the pixel matrix data in order to automatically identify adifference in the intensity data values between pixels.

In the example of FIG. 6, sub-region 504 is a region having an intensityexceeding a threshold level or value to be considered “high” relative toother regions within the target contour 404. Using inputs correspondingto differences in pixel image intensity, sub-region 504 can beautomatically identified and a corresponding contour 609 generated sothat a different dose constraint may be applied to sub-region 504relative to the other sub-regions within target contour 404. A treatmentplanning system receives the contour 609 for sub-region 504 and itscorresponding dose constraints as input and then generates a treatmentplan.

The dose distribution is an important parameter in external beamradiation treatment. If a radiation dose were too low in a dense andactive sub-region because the radiation is spread over a higher thanexpected number of cells, then the radiation treatment could beineffective. If a radiation dose were too high at a particular point inthe tissue, the radiation treatment might have negative effects. Assuch, the intensity data from the functional image (e.g., PET) allowsfor additional constraints to be applied to an inverse planning systemin such a way that conformality of dose to the treatment target isrewarded. For example, the intra-cranial region containing lesion 403and higher cell density region (corresponding to higher intensity region504) each receive an appropriate dose distribution within prescribedlimits while minimizing the dose and, thereby, damage to surroundinghealthy tissue.

One method of applying a dose constraint involves defining an acceptablerange for a dose value (Dv) between a minimum dose (D_(min)) value and amaximum dose (D_(max)) value and can be represented as:D_(min)≦Dv≦D_(max). Dose constraints are user-specified and can beapplied to manually delineated and automatically generated regions fromanatomical and/or functional images. The minimum and maximum dose valuesare a function of the intensity value (Ip) of the pixels within thisregion (e.g., matrix 308) and, in one embodiment, may be representedusing functions, for example, as D_(min) (f(Ip))≦Dv≦D_(max) (g(Ip)), inwhich (f(Ip)) influences minimum dose and (g(Ip)) influences maximumdose.

FIG. 7A illustrates an exemplary graph 700 showing minimum dose as afunction of intensity, such that as the intensity value increases, theminimum dose value of an acceptable dose range increases. Similarly, themaximum dose value also increases as the intensity value increases.Alternatively, other means, such as a look-up table, may be used todetermine the dose constraints based on intensity. The generation of acontour and a treatment plan based on the input of contours and doseconstraints in known in the art; accordingly, a more detaileddescription is not provided herein.

FIG. 7B is a graph 710 illustrating the difference between three exampledose volume histograms (DVHs), with each DVH corresponding to conditionswith or without functional input from PET image 500. A DVH is acalculated curve that yields the volume percentage receiving aparticular radiation dose (in cGy) within the VOI. Ideally, the DVH is arectangular function, in which the dose is 100 percent of the prescribeddose over the volume of the lesion and zero in non-lesion regions. Curve701 corresponds to an example calculation from a treatment plan thatdoes not include an input from a functional imaging modality thatprovides intensity data indicative of cell concentration. Curve 702corresponds to an example calculation from a treatment plan thatincludes a boost in dose distribution in sub-region 504. Curve 703corresponds to an example calculation for sub-region 504 only. Curve702, which includes a dose boost based on functional image data, has acloser rectangular function relative to curve 701, which does notreflect any dose boost. This difference indicates that a greater amountof lesion cells are exposed and treated based on the dose constraintprovided for the higher intensity data. Curve 703 shows the bestrectangular function because that coverage is limited only to the boostarea of the lesion.

As previously noted, two imaging modalities may not be needed and that ahigher intensity region and its corresponding dose constraints can bedetermined from a single image modality. For example, intensity levelsdisplayed by a PET image for a target lesion can be the sole basis forinverse planning. Moreover, PET images are just one type of functionalimages that display differences in intensity levels for a target lesion.Single photon emission computed tomography (SPECT), fMRI, and nuclearmagnetic resonance (NMR) imaging are other types of functional imagesthat can generate inputs for inverse planning.

FIG. 8 is a flowchart illustrating one embodiment of a method of inversetreatment planning. Flowchart 800 is described with respect to anexample of delivering a radiation dose to a lesion located within thebrain of a patient but the method of the present invention is not solimited and may be applied to the delivery of radiation dose to otherpathological anatomies in other portions of the patient.

In one embodiment, anatomical data of the lesion is obtained byacquiring an anatomical image (e.g., CT) to form a three-dimensionalview of the lesion and the surrounding tissue, step 810. An exemplary CTimage is the axial slice of a patient's brain as shown above withrespect to CT image 400 of FIG. 3. The CT image shows the location andsize of the lesion (e.g., 403) and its surrounding tissue. The lesionregion may also be analyzed with functional data from an acquiredfunctional image (e.g., PET), step 820. An exemplary PET image is PETimage 500 of FIG. 4. The PET image shows the metabolic activity of thescanned region, and in particular, the degree of cellular activitywithin various portions of the lesion. Regions of high metabolicactivity are depicted as relatively bright regions (e.g., region 504).

In one embodiment, the anatomical (e.g., CT) image and the functional(e.g., PET) image are correlated (e.g., by overlay) with each other,step 830, to combine the data derived from each image modality.Alternatively, no correlation may be performed and no anatomical imageneed be generated, as previously discussed above.

At step 840, the identification of one or more regions of differingintensity in the functional image is performed automatically, asdiscussed above. An algorithm is used to automatically identify one ormore regions of differing (e.g., higher) intensity within the delineatedcontour of the lesion and, in step 850, one or more correspondingcontours for the differing intensity regions are generated. In step 860,dose constraints may be applied for the generated contours. For example,a higher dose volume constraint can be applied to automaticallyidentified higher intensity region 504 (e.g., the higher cell densityregion), while a lower dose constraint can be applied to the other areasof lesion 403 (the area outside of contour 609 but within contour 404).Other types of constraints can be applied to organs or otherwise healthytissue surrounding the target lesion based on the CT image.

FIG. 9 illustrates one embodiment of medical diagnostic imaging andinverse planning system 900 in which features of the present inventionmay be implemented. The medical diagnostic imaging system 900 isdiscussed below at times in relation to anatomical and functionalimaging modalities (e.g., CT and PET) only for ease of explanation.However, other imaging modalities may be used as previously mentioned.

Medical diagnostic imaging system 900 includes one or more imagingsources 904, 905 to generate a beam (e.g., kilovoltage x-rays, megavoltage x-rays, ultrasound, MRI, PET, etc.) and one or morecorresponding imagers 905, 906 to detect and receive the beam generatedby imaging sources 904, 905. For example, imager 905 can correspond to aCT imager and imager 906 can correspond to a PET imager. Imaging sources904, 905 and the imagers 905, 906 are coupled to a digital processingsystem 910 to control the imaging operation. Digital processing system910 includes a bus or other means 911 for transferring data amongcomponents of digital processing system 910. Digital processing system910 also includes a processing device 901. Processing device 901 mayrepresent one or more general-purpose processors (e.g., amicroprocessor), special purpose processor such as a digital signalprocessor (DSP) or other type of device such as a controller or fieldprogrammable gate array (FPGA). Processing device 901 may be configuredto execute the instructions for performing the operations and stepsdiscussed herein. In particular, processing device 901 may be configuredto execute instructions to automatically delineate and constrain regionsof high intensity (e.g., sub-region 305) in the target region to guidedose distribution. For example, sub-region 305 can be automaticallyadjusted to receive a higher dose of radiation and have a biggeracceptable dose range.

Digital processing system 910 may also include system memory 902 thatmay include a random access memory (RAM), or other dynamic storagedevice, coupled to bus 911 for storing information and instructions tobe executed by processing device 910. System memory 902 also may be usedfor storing temporary variables or other intermediate information duringexecution of instructions by processing device 910. System memory 902may also include a read only memory (ROM) and/or other static storagedevice coupled to bus 911 for storing static information andinstructions for processing device 910.

A storage device 903 represents one or more storage devices (e.g., amagnetic disk drive or optical disk drive) coupled to bus 911 forstoring information and instructions. Storage device 903 may be used forstoring instructions for performing the steps discussed herein.

Digital processing system 910 may also be coupled to a display device907, such as a cathode ray tube (CRT) or liquid crystal display (LCD),for displaying information (e.g., three-dimensional representation ofthe VOI) to the user. An input device 908, such as a keyboard, may becoupled to digital processing system 910 for communicating informationand/or command selections to processing device 910. One or more otheruser input devices, such as a mouse, a trackball, or cursor directionkeys for communicating direction infor mation and command selections toprocessing device 910 and for controlling cursor movement on display 907may also be used.

Digital processing system 910 represents only one example of a system,which may have many different configurations and architectures, andwhich may be employed with the present invention. For example, somesystems often have multiple buses, such as a peripheral bus, a dedicatedcache bus, etc.

One or more of the components of digital processing system 910 may forma treatment planning system. The treatment planning system may share itsdatabase (e.g., stored in storage device 903) with a treatment deliverysystem, so that it is not necessary to export from the treatmentplanning system prior to treatment delivery. The treatment planningsystem may also include MIRIT (Medical Image Review and Import Tool) tosupport DICOM import (so images can be fused and targets delineated ondifferent systems and then imported into the treatment planning systemfor planning and dose calculations), expanded image fusion capabilitiesthat allow the user to plan and view isodose distributions on any one ofvarious imaging modalities (e.g., MRI, CT, PET, etc.).

In one embodiment, the treatment delivery system may be a frame-lessrobotic based linear accelerator (LINAC) radiosurgery system, such asthe CyberKnife® system developed by Accuray, Inc. of California. In sucha system, the LINAC is mounted on the end of a robotic arm havingmultiple (e.g., 5 or more) degrees of freedom in order to position theLINAC to irradiate the pathological anatomy with beams delivered frommany angles in an operating volume (e.g., sphere) around the patient.Treatment may involve beam paths with a single isocenter, multipleisocenters, or with a non-isocentric approach (i.e., the beams need onlyintersect with the pathological target volume and do not necessarilyconverge on a single point, or isocenter, within the target). Treatmentcan be delivered in either a single session (mono-fraction) or in asmall number of sessions (hypo-fractionation) as determined duringtreatment planning.

Alternatively, another type of treatment delivery systems may be used,for example, a gantry based (isocentric) intensity modulatedradiotherapy (IMRT) system. In a gantry based system, a radiation source(e.g., a LINAC) is mounted on the gantry in such a way that it rotatesin a plane corresponding to an axial slice of the patient. Radiation isthen delivered from several positions on the circular plane of rotation.In IMRT, the shape of the radiation beam is defined by a multi-leafcollimator that allows portions of the beam to be blocked, so that theremaining beam incident on the patient has a pre-defined shape. In theIMRT planning, the optimization algorithm of selects subsets of the mainbeam and determines the amount of time for which the subset of beamsshould be exposed, so that the dose constraints are best met.

In another embodiment, yet other types of treatment delivery systems maybe used, for example, a stereotactic frame system such as theGammaKnife®, available from Elekta of Sweden. With such a system, theforward planning optimization algorithm (also referred to as a spherepacking algorithm) of the treatment plan determines the selection anddose weighting assigned to a group of beams forming isocenters in orderto best meet provided dose constraints.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

1. A method, comprising: receiving a functional image including a firstregion of a pathological anatomy comprising first pixels and a secondregion of the pathological anatomy comprising second pixels, each of thefirst and second pixels having a corresponding intensity data value; andautomatically distinguishing between the first region and the secondregion based on a difference between the intensity data values of thefirst and second pixels.
 2. The method of claim 1, wherein the intensitydata values of the first pixels are greater than the intensity datavalues of the second pixels and wherein automatically distinguishingcomprises determining that more of the first pixels have intensity datavalues being greater than intensity data values of the second pixels. 3.The method of claim 1, wherein the difference between the intensityvalues is with respect to a threshold intensity value.
 4. The method ofclaim 3 wherein automatically distinguishing comprises: comparing theintensity data values of the first pixels against the thresholdintensity value; and determining that the intensity data values of thefirst pixels are greater than the threshold intensity value.
 5. Themethod of claim 4, wherein automatically distinguishing comprises:comparing the intensity data values of the second pixels against thethreshold intensity value; and determining that the intensity datavalues of the second pixels are less than the threshold intensity value.6. The method of claim 2, further comprises associating a higher celldistribution within the pathological anatomy with the first region and alower cell distribution with the second region.
 7. The method of claim1, further comprising: generating a first contour corresponding to thefirst region using the intensity data values of the first pixels; andgenerating a second contour corresponding to the second region using theintensity data values of the second pixels.
 8. The method of claim 7,further comprises: applying a first radiation dose constraint for thefirst contour; and applying a second radiation dose constraint for thesecond region.
 9. The method of claim 1, wherein automaticallydistinguishing is performed without visual inspection of the functionalimage by a person.
 10. The method of claim 1, wherein the function imageis a positron emission tomography (PET) image.
 11. The method of claim1, further comprising: receiving an anatomical image; identifying thepathological anatomy in the anatomical image; and correlating thefunctional image with the anatomical image.
 12. The method of claim 11,wherein the anatomical image is a CT image.
 13. The method of claim 11,wherein correlating comprises overlaying the functional image with theanatomical image.
 14. The method of claim 11, wherein correlatingcomprises acquiring the functional image in a same space as theanatomical image.
 15. The method of claim 1, further comprisingacquiring the functional image.
 16. A method, comprising: acquiring a CTimage of a pathological anatomy; identifying the pathological anatomy inthe CT image, acquiring a PET image including a first region of thepathological anatomy comprising first pixels and a second region of thepathological anatomy comprising second pixels, each of the first andsecond pixels having a corresponding intensity data value; automaticallydistinguishing between the first region and the second region based on adifference between the intensity data values of the first and secondpixels; generating a first contour corresponding to the first regionusing the intensity data values of the first pixels; and generating asecond contour corresponding to the second region using the intensitydata values of the second pixels.
 17. The method of claim 16, furthercomprising correlating the functional image with the anatomical image.18. The method of claim 16, further comprising: applying a firstradiation dose constraint for use within the first contour; and applyinga second radiation dose constraint for use within the second contour.19. The method of claim 18, wherein the first region corresponds to ahigher cell density with respect to the second region and the secondregion corresponds to a lower cell density with respect to the firstregion.
 20. An apparatus, comprising: means for acquiring a functionalimage; and means for automatically identifying a region of differingintensity relative to other regions in the functional image.
 21. Theapparatus of claim 20, further comprising: means for acquiring ananatomical image; and means for correlating the functional image withthe anatomical image.
 22. The apparatus of claim 20, wherein the meansfor automatically identifying further comprises means for determiningwhether pixel intensity values fall above or below a threshold pixelintensity value.
 23. A machine readable medium having instructionsthereon, which when executed by a processor, cause the processor toperform the following comprising: receiving a functional image includinga first region of a pathological anatomy comprising first pixels and asecond region of the pathological anatomy comprising second pixels, eachof the first and second pixels having a corresponding intensity datavalue; and automatically distinguishing between the first region and thesecond region based on a difference between the intensity data values ofthe first and second pixels.
 24. The machine readable medium of claim23, wherein the intensity data values of the first pixels are greaterthan the intensity data values of the second pixels and whereinautomatically distinguishing comprises determining that more of thefirst pixels have intensity data values being greater than intensitydata values of the second pixels.
 25. The machine readable medium ofclaim 23, wherein the difference between the intensity values is withrespect to a threshold intensity value.
 26. The machine readable mediumof claim 24, wherein automatically distinguishing comprises: comparingthe intensity data values of the first pixels against a threshold value;and determining that the intensity data values of the first pixels aregreater than the threshold value.
 27. The machine readable medium ofclaim 26, wherein automatically distinguishing comprises: comparing theintensity data values of the second pixels against the threshold value;and determining that the intensity data values of the second pixels areless than the threshold value.
 28. The machine readable medium of claim24, further comprises associating a higher cell distribution within thepathological anatomy with the first region and a lower cell distributionwith the second region.
 29. The machine readable medium of claim 23,wherein automatically distinguishing further comprises: ranking thefirst pixels and the second pixels from a lowest intensity data value toa highest intensity data value; assigning a threshold intensity valuethat is between the lowest intensity value and the highest intensityvalue; and flagging a pixel as part of a high intensity area if greaterthan the threshold intensity value.
 30. The apparatus of claim 20,wherein means for automatically identifying further comprises means forcomparing a pixel intensity value with a threshold value.
 31. Anapparatus, comprising: a memory to store a plurality of functionalimages including a first region of a pathological anatomy comprisingfirst pixels and a second region of the pathological anatomy comprisingsecond pixels, each of the first and second pixels having acorresponding intensity data value; and a processor coupled to thememory receive the plurality of functional images, the processor toautomatically distinguish between the first region and the second regionbased on a difference between the intensity data values of the first andsecond pixels.
 32. The apparatus of claim 31, further comprising animager coupled to the memory, the imager to generate the plurality offunctional images.
 33. The apparatus of claim 31, wherein the intensitydata values of the first pixels are greater than the intensity datavalues of the second pixels and wherein automatically distinguishingcomprises determining that more of the first pixels have intensity datavalues being greater than intensity data values of the second pixels.34. The apparatus of claim 31, wherein the difference between theintensity values is with respect to a threshold intensity value.
 35. Theapparatus of claim 34, wherein the processor is configured to comparethe intensity data values of the first pixels against the thresholdintensity value and determine if the intensity data values of the firstpixels are greater than the threshold intensity value.
 36. The apparatusof claim 35, wherein the processor is configured to compare theintensity data values of the second pixels against the thresholdintensity value and determine if the intensity data values of the secondpixels are less than the threshold intensity value.